I have recently enrolled to Introduction to Data Science. One of the very first assignments was Twitter sentiment analysis performed in Python. Leaving a whole lot aside, what captured my attention was a requirement to resolve tweets’ geocoded locations without relying on 3rd party services.
The assignment paper suggested to use a Python Dictionary of State Abbreviations. That proved helpful indeed. I have decided to combine this resource with Average Latitude and Longitude for US States and ended up with a single dictionary containing all essential information, i.e. state codes, names and coordinates:
[python]
{
‘AK’: {‘name’:’Alaska’,’coords’:[61.3850,-152.2683]},
‘AL’: {‘name’:’Alabama’,’coords’:[32.7990,-86.8073]},
‘AR’: {‘name’:’Arkansas’,’coords’:[34.9513,-92.3809]},
‘AS’: {‘name’:’American Samoa’,’coords’:[14.2417,-170.7197]},
‘AZ’: {‘name’:’Arizona’,’coords’:[33.7712,-111.3877]},
‘CA’: {‘name’:’California’,’coords’:[36.1700,-119.7462]},
‘CO’: {‘name’:’Colorado’,’coords’:[39.0646,-105.3272]},
‘CT’: {‘name’:’Connecticut’,’coords’:[41.5834,-72.7622]},
‘DC’: {‘name’:’District of Columbia’,’coords’:[38.8964,-77.0262]},
‘DE’: {‘name’:’Delaware’,’coords’:[39.3498,-75.5148]},
‘FL’: {‘name’:’Florida’,’coords’:[27.8333,-81.7170]},
‘GA’: {‘name’:’Georgia’,’coords’:[32.9866,-83.6487]},
‘HI’: {‘name’:’Hawaii’,’coords’:[21.1098,-157.5311]},
‘IA’: {‘name’:’Iowa’,’coords’:[42.0046,-93.2140]},
‘ID’: {‘name’:’Idaho’,’coords’:[44.2394,-114.5103]},
‘IL’: {‘name’:’Illinois’,’coords’:[40.3363,-89.0022]},
‘IN’: {‘name’:’Indiana’,’coords’:[39.8647,-86.2604]},
‘KS’: {‘name’:’Kansas’,’coords’:[38.5111,-96.8005]},
‘KY’: {‘name’:’Kentucky’,’coords’:[37.6690,-84.6514]},
‘LA’: {‘name’:’Louisiana’,’coords’:[31.1801,-91.8749]},
‘MA’: {‘name’:’Massachusetts’,’coords’:[42.2373,-71.5314]},
‘MD’: {‘name’:’Maryland’,’coords’:[39.0724,-76.7902]},
‘ME’: {‘name’:’Maine’,’coords’:[44.6074,-69.3977]},
‘MI’: {‘name’:’Michigan’,’coords’:[43.3504,-84.5603]},
‘MN’: {‘name’:’Minnesota’,’coords’:[45.7326,-93.9196]},
‘MO’: {‘name’:’Missouri’,’coords’:[38.4623,-92.3020]},
‘MP’: {‘name’:’Northern Mariana Islands’,’coords’:[14.8058,145.5505]},
‘MS’: {‘name’:’Mississippi’,’coords’:[32.7673,-89.6812]},
‘MT’: {‘name’:’Montana’,’coords’:[46.9048,-110.3261]},
‘NC’: {‘name’:’North Carolina’,’coords’:[35.6411,-79.8431]},
‘ND’: {‘name’:’North Dakota’,’coords’:[47.5362,-99.7930]},
‘NE’: {‘name’:’Nebraska’,’coords’:[41.1289,-98.2883]},
‘NH’: {‘name’:’New Hampshire’,’coords’:[43.4108,-71.5653]},
‘NJ’: {‘name’:’New Jersey’,’coords’:[40.3140,-74.5089]},
‘NM’: {‘name’:’New Mexico’,’coords’:[34.8375,-106.2371]},
‘NV’: {‘name’:’Nevada’,’coords’:[38.4199,-117.1219]},
‘NY’: {‘name’:’New York’,’coords’:[42.1497,-74.9384]},
‘OH’: {‘name’:’Ohio’,’coords’:[40.3736,-82.7755]},
‘OK’: {‘name’:’Oklahoma’,’coords’:[35.5376,-96.9247]},
‘OR’: {‘name’:’Oregon’,’coords’:[44.5672,-122.1269]},
‘PA’: {‘name’:’Pennsylvania’,’coords’:[40.5773,-77.2640]},
‘PR’: {‘name’:’Puerto Rico’,’coords’:[18.2766,-66.3350]},
‘RI’: {‘name’:’Rhode Island’,’coords’:[41.6772,-71.5101]},
‘SC’: {‘name’:’South Carolina’,’coords’:[33.8191,-80.9066]},
‘SD’: {‘name’:’South Dakota’,’coords’:[44.2853,-99.4632]},
‘TN’: {‘name’:’Tennessee’,’coords’:[35.7449,-86.7489]},
‘TX’: {‘name’:’Texas’,’coords’:[31.1060,-97.6475]},
‘UT’: {‘name’:’Utah’,’coords’:[40.1135,-111.8535]},
‘VA’: {‘name’:’Virginia’,’coords’:[37.7680,-78.2057]},
‘VI’: {‘name’:’Virgin Islands’,’coords’:[18.0001,-64.8199]},
‘VT’: {‘name’:’Vermont’,’coords’:[44.0407,-72.7093]},
‘WA’: {‘name’:’Washington’,’coords’:[47.3917,-121.5708]},
‘WI’: {‘name’:’Wisconsin’,’coords’:[44.2563,-89.6385]},
‘WV’: {‘name’:’West Virginia’,’coords’:[38.4680,-80.9696]},
‘WY’: {‘name’:’Wyoming’,’coords’:[42.7475,-107.2085]}
}
[/python]
Having all the relevant information in place, I was looking for a feasible way of associating the tweets with the list of US states. Turns out that Haversine formula is one of the most popular methods for calculating distance between two pairs of coordinates.
My implementation of the Haversine formula merely mirrors the unbeatable Python example at platoscave.net:
[python]
def haversine(self, origin, destination):
# two pairs of latitude and longitude, i.e. origin vs destination
lat1, lon1 = origin
lat2, lon2 = destination
# deltas between origin and destination coordinates
dlat = math.radians(lat2-lat1)
dlon = math.radians(lon2-lon1)
# a central angle between the two points
a = math.sin(dlat/2) * math.sin(dlat/2) + math.cos(math.radians(lat1))
* math.cos(math.radians(lat2)) * math.sin(dlon/2) * math.sin(dlon/2)
# the determinative angle of the triangle on the surface of the sphere (Earth)
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))
# a spherical distance between the two points, i.e. hills etc are not considered
# self.R = 6371 (Earth’s mean radius in km)
return self.R * c
[/python]
The algorithm above is the core of my custom search method, which simply picks up the state which closely matches the provided coordinates (a minimum distance). To eliminate non-US countries, I have set a hard limit of 500 km as a maximum distance between the provided coordinates and the average coordinates of any of the states:
[python]
def by_coords(self, latitude, longitude):
distances = {}
for k, v in self.states.iteritems():
coords = v[‘coords’]
distance = self.haversine(coords, [latitude, longitude])
distances[k] = distance
shortest_distance = min(distances.values())
# self.max_distance is 500 km
if shortest_distance > self.max_distance:
return None
else:
return [ k for k, v in distances.items() if v == shortest_distance ][0]
[/python]
This leaves me with a nice and handy feature:
[python]
def main():
us_states = USStates()
# Sacramento, California – prints CA
print us_states.by_coords(38.3454, -121.2935)
# Austin, Texas – prints TX
print us_states.by_coords(30.25, -97.75)
# New Delhi, India – yields no results
# as the minimum calculated distance is well over 13.000 km
print us_states.by_coords(28.6139, 77.2089)
[/python]
The coordinates comprise latitude and longitude using the convention of a signed decimal degrees without compass direction. Negative numbers represent south or west, examples:
[python]
# latitudes:
# 30° 45´ 50´´N -> 30.4550
# 28° 61´ 39´´S -> -28.6139
#
# longitudes:
# 77° 20´ 89´´E -> 77.2089
# 30° 45´ 50´´W -> -30.4550
[/python]
